Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes
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arXiv
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| Format: | Preprint |
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2024
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| author | Javed, Saqib Khan, Ahmad Jarrar Dumery, Corentin Zhao, Chen Salzmann, Mathieu |
| author_facet | Javed, Saqib Khan, Ahmad Jarrar Dumery, Corentin Zhao, Chen Salzmann, Mathieu |
| contents | Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory usage and improvements in rendering efficiency are required. While many state-of-the-art methods prioritize lightweight implementations, they struggle in handling {scenes with complex motions or long sequences}. In this work, we introduce Temporally Compressed 3D Gaussian Splatting (TC3DGS), a novel technique designed specifically to effectively compress dynamic 3D Gaussian representations. TC3DGS selectively prunes Gaussians based on their temporal relevance and employs gradient-aware mixed-precision quantization to dynamically compress Gaussian parameters. In addition, TC3DGS exploits an adapted version of the Ramer-Douglas-Peucker algorithm to further reduce storage by interpolating Gaussian trajectories across frames. Our experiments on multiple datasets demonstrate that TC3DGS achieves up to 67$\times$ compression with minimal or no degradation in visual quality. More results and videos are provided in the supplementary. Project Page: https://ahmad-jarrar.github.io/tc-3dgs/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes Javed, Saqib Khan, Ahmad Jarrar Dumery, Corentin Zhao, Chen Salzmann, Mathieu Computer Vision and Pattern Recognition Graphics Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory usage and improvements in rendering efficiency are required. While many state-of-the-art methods prioritize lightweight implementations, they struggle in handling {scenes with complex motions or long sequences}. In this work, we introduce Temporally Compressed 3D Gaussian Splatting (TC3DGS), a novel technique designed specifically to effectively compress dynamic 3D Gaussian representations. TC3DGS selectively prunes Gaussians based on their temporal relevance and employs gradient-aware mixed-precision quantization to dynamically compress Gaussian parameters. In addition, TC3DGS exploits an adapted version of the Ramer-Douglas-Peucker algorithm to further reduce storage by interpolating Gaussian trajectories across frames. Our experiments on multiple datasets demonstrate that TC3DGS achieves up to 67$\times$ compression with minimal or no degradation in visual quality. More results and videos are provided in the supplementary. Project Page: https://ahmad-jarrar.github.io/tc-3dgs/ |
| title | Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2412.05700 |